{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the [CartPole-v1](https://gym.openai.com/envs/CartPole-v0/) OpenAI Gym environment. For reproducibility, let is fix a random seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:04.814474Z",
     "start_time": "2021-01-06T00:35:03.521659Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0105 163503.868 dataclasses.py:48] USE_VANILLA_DATACLASS: True\n",
      "I0105 163503.869 dataclasses.py:49] ARBITRARY_TYPES_ALLOWED: True\n",
      "W0105 163503.876 file_io.py:72] ** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to the version in iopath repo. **\n",
      "https://github.com/facebookresearch/iopath \n",
      "\n",
      "W0105 163503.889 manifold.py:84] ** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to iopath. **\n",
      "\n",
      "I0105 163503.890 io.py:19] Registered Manifold PathManager\n",
      "W0105 163503.891 manifold.py:84] ** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to iopath. **\n",
      "\n",
      "I0105 163503.891 patch.py:95] Patched torch.load, torch.save, torch.jit.load and save to handle Manifold uri\n",
      "I0105 163504.187 registry_meta.py:19] Adding REGISTRY to type TrainingReport\n",
      "I0105 163504.188 registry_meta.py:40] Not Registering TrainingReport to TrainingReport. Abstract method [] are not implemented.\n",
      "I0105 163504.189 registry_meta.py:19] Adding REGISTRY to type PublishingResult\n",
      "I0105 163504.189 registry_meta.py:40] Not Registering PublishingResult to PublishingResult. Abstract method [] are not implemented.\n",
      "I0105 163504.190 registry_meta.py:19] Adding REGISTRY to type ValidationResult\n",
      "I0105 163504.191 registry_meta.py:40] Not Registering ValidationResult to ValidationResult. Abstract method [] are not implemented.\n",
      "I0105 163504.191 registry_meta.py:31] Registering NoPublishingResults to PublishingResult\n",
      "I0105 163504.192 registry_meta.py:34] Using no_publishing_results instead of NoPublishingResults\n",
      "I0105 163504.193 registry_meta.py:31] Registering NoValidationResults to ValidationResult\n",
      "I0105 163504.193 registry_meta.py:34] Using no_validation_results instead of NoValidationResults\n",
      "I0105 163504.198 registry_meta.py:31] Registering SchedulingFrequencyValidationResults to ValidationResult\n",
      "I0105 163504.199 registry_meta.py:34] Using scheduling_frequency_validation_results instead of SchedulingFrequencyValidationResults\n",
      "I0105 163504.200 registry_meta.py:31] Registering PDIVFilterValidationResults to ValidationResult\n",
      "I0105 163504.201 registry_meta.py:34] Using pdiv_filter_validation_results instead of PDIVFilterValidationResults\n",
      "I0105 163504.201 registry_meta.py:31] Registering Seq2SlateValidationResults to ValidationResult\n",
      "I0105 163504.202 registry_meta.py:34] Using seq2slate_validation_results instead of Seq2SlateValidationResults\n",
      "I0105 163504.203 registry_meta.py:31] Registering SchedulingFrequencyPublishingResults to PublishingResult\n",
      "I0105 163504.203 registry_meta.py:34] Using scheduling_frequency_publishing_results instead of SchedulingFrequencyPublishingResults\n",
      "I0105 163504.204 registry_meta.py:31] Registering PDIVFilterPublishingResults to PublishingResult\n",
      "I0105 163504.205 registry_meta.py:34] Using pdiv_filter_publishing_results instead of PDIVFilterPublishingResults\n",
      "I0105 163504.206 registry_meta.py:31] Registering FeedPublishingResults to PublishingResult\n",
      "I0105 163504.207 registry_meta.py:34] Using feed_publishing_results instead of FeedPublishingResults\n",
      "I0105 163504.208 registry_meta.py:31] Registering ScoreFblearnerPredictorPublishingResult to PublishingResult\n",
      "I0105 163504.208 registry_meta.py:34] Using score_offline_results instead of ScoreFblearnerPredictorPublishingResult\n",
      "I0105 163504.209 registry_meta.py:31] Registering ScoreSeq2SlateOutput to PublishingResult\n",
      "I0105 163504.210 registry_meta.py:34] Using score_seq2slate_offline instead of ScoreSeq2SlateOutput\n",
      "I0105 163504.211 registry_meta.py:31] Registering SlateRewardFeatureImportanceOutput to PublishingResult\n",
      "I0105 163504.212 registry_meta.py:34] Using slate_reward_feature_importance instead of SlateRewardFeatureImportanceOutput\n",
      "I0105 163504.214 dataclasses.py:73] Setting IdMapping.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.215 dataclasses.py:73] Setting ModelFeatureConfig.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.244 registry_meta.py:19] Adding REGISTRY to type LearningRateSchedulerConfig\n",
      "I0105 163504.245 registry_meta.py:40] Not Registering LearningRateSchedulerConfig to LearningRateSchedulerConfig. Abstract method [] are not implemented.\n",
      "I0105 163504.247 registry_meta.py:19] Adding REGISTRY to type OptimizerConfig\n",
      "I0105 163504.247 registry_meta.py:40] Not Registering OptimizerConfig to OptimizerConfig. Abstract method [] are not implemented.\n",
      "I0105 163504.248 registry_meta.py:31] Registering Adam to OptimizerConfig\n",
      "I0105 163504.250 registry_meta.py:31] Registering SGD to OptimizerConfig\n",
      "I0105 163504.251 registry_meta.py:31] Registering AdamW to OptimizerConfig\n",
      "I0105 163504.252 registry_meta.py:31] Registering SparseAdam to OptimizerConfig\n",
      "I0105 163504.253 registry_meta.py:31] Registering Adamax to OptimizerConfig\n",
      "I0105 163504.255 registry_meta.py:31] Registering LBFGS to OptimizerConfig\n",
      "I0105 163504.256 registry_meta.py:31] Registering Rprop to OptimizerConfig\n",
      "I0105 163504.258 registry_meta.py:31] Registering ASGD to OptimizerConfig\n",
      "I0105 163504.259 registry_meta.py:31] Registering Adadelta to OptimizerConfig\n",
      "I0105 163504.260 registry_meta.py:31] Registering Adagrad to OptimizerConfig\n",
      "I0105 163504.261 registry_meta.py:31] Registering RMSprop to OptimizerConfig\n",
      "I0105 163504.444 dataclasses.py:73] Setting Seq2SlateNet.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.471 registry_meta.py:19] Adding REGISTRY to type EnvWrapper\n",
      "I0105 163504.472 registry_meta.py:40] Not Registering EnvWrapper to EnvWrapper. Abstract method ['serving_obs_preprocessor', 'make', 'obs_preprocessor'] are not implemented.\n",
      "I0105 163504.472 dataclasses.py:73] Setting EnvWrapper.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.476 registry_meta.py:31] Registering ChangingArms to EnvWrapper\n",
      "I0105 163504.489 registry_meta.py:31] Registering Gym to EnvWrapper\n",
      "I0105 163504.492 utils.py:18] Registering id=Pocman-v0, entry_point=reagent.gym.envs.pomdp.pocman:PocManEnv.\n",
      "I0105 163504.493 utils.py:18] Registering id=StringGame-v0, entry_point=reagent.gym.envs.pomdp.string_game:StringGameEnv.\n",
      "I0105 163504.494 utils.py:18] Registering id=LinearDynamics-v0, entry_point=reagent.gym.envs.dynamics.linear_dynamics:LinDynaEnv.\n",
      "I0105 163504.494 utils.py:18] Registering id=PossibleActionsMaskTester-v0, entry_point=reagent.gym.envs.functionality.possible_actions_mask_tester:PossibleActionsMaskTester.\n",
      "I0105 163504.517 registry_meta.py:31] Registering RecSim to EnvWrapper\n",
      "I0105 163504.518 dataclasses.py:73] Setting RecSim.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.520 registry_meta.py:31] Registering OraclePVM to EnvWrapper\n",
      "I0105 163504.521 dataclasses.py:73] Setting OraclePVM.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.527 registry_meta.py:31] Registering ToyVM to EnvWrapper\n",
      "\n",
      "Bad key \"axes.color_cycle\" on line 214 in\n",
      "/home/alexnik/.matplotlib/matplotlibrc.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "from reagent.gym.envs.gym import Gym\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import tqdm.autonotebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:04.868793Z",
     "start_time": "2021-01-06T00:35:04.816545Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0105 163504.822 env_wrapper.py:38] Env: <TimeLimit<CartPoleEnv<CartPole-v0>>>;\n",
      "observation_space: Box(4,);\n",
      "action_space: Discrete(2);\n"
     ]
    }
   ],
   "source": [
    "env = Gym('CartPole-v0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:04.924801Z",
     "start_time": "2021-01-06T00:35:04.871353Z"
    }
   },
   "outputs": [],
   "source": [
    "def reset_env(env, seed):\n",
    "    np.random.seed(seed)\n",
    "    env.seed(seed)\n",
    "    env.action_space.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    env.reset()\n",
    "\n",
    "reset_env(env, seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `policy` is composed of a simple scorer (a MLP) and a softmax sampler. Our `agent` simply executes this policy in the CartPole Environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:05.032238Z",
     "start_time": "2021-01-06T00:35:04.927177Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0105 163504.970 registry_meta.py:19] Adding REGISTRY to type DiscreteDQNNetBuilder\n",
      "I0105 163504.972 registry_meta.py:40] Not Registering DiscreteDQNNetBuilder to DiscreteDQNNetBuilder. Abstract method ['build_q_network'] are not implemented.\n",
      "I0105 163504.973 registry_meta.py:31] Registering Dueling to DiscreteDQNNetBuilder\n",
      "I0105 163504.973 dataclasses.py:73] Setting Dueling.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.975 registry_meta.py:31] Registering FullyConnected to DiscreteDQNNetBuilder\n",
      "I0105 163504.976 dataclasses.py:73] Setting FullyConnected.__post_init__ to its __post_init_post_parse__\n",
      "I0105 163504.978 registry_meta.py:31] Registering FullyConnectedWithEmbedding to DiscreteDQNNetBuilder\n",
      "I0105 163504.978 dataclasses.py:73] Setting FullyConnectedWithEmbedding.__post_init__ to its __post_init_post_parse__\n"
     ]
    }
   ],
   "source": [
    "from reagent.net_builder.discrete_dqn.fully_connected import FullyConnected\n",
    "from reagent.gym.utils import build_normalizer\n",
    "\n",
    "norm = build_normalizer(env)\n",
    "net_builder = FullyConnected(sizes=[8], activations=[\"linear\"])\n",
    "cartpole_scorer = net_builder.build_q_network(\n",
    "    state_feature_config=None, \n",
    "    state_normalization_data=norm['state'],\n",
    "    output_dim=len(norm['action'].dense_normalization_parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:05.086918Z",
     "start_time": "2021-01-06T00:35:05.034100Z"
    }
   },
   "outputs": [],
   "source": [
    "from reagent.gym.policies.policy import Policy\n",
    "from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler\n",
    "from reagent.gym.agents.agent import Agent\n",
    "\n",
    "\n",
    "policy = Policy(scorer=cartpole_scorer, sampler=SoftmaxActionSampler())\n",
    "agent = Agent.create_for_env(env, policy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a trainer that uses the REINFORCE Algorithm to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:05.146567Z",
     "start_time": "2021-01-06T00:35:05.088972Z"
    }
   },
   "outputs": [],
   "source": [
    "from reagent.training.reinforce import (\n",
    "    Reinforce, ReinforceParams\n",
    ")\n",
    "from reagent.optimizer.union import classes\n",
    "\n",
    "\n",
    "trainer = Reinforce(policy, ReinforceParams(\n",
    "    gamma=0.99,\n",
    "    optimizer=classes['Adam'](lr=5e-3, weight_decay=1e-3)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the trajectory of observed transitions into a training batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:05.198092Z",
     "start_time": "2021-01-06T00:35:05.148592Z"
    }
   },
   "outputs": [],
   "source": [
    "import reagent.types as rlt\n",
    "\n",
    "def to_train_batch(trajectory):\n",
    "    return rlt.PolicyGradientInput(\n",
    "        state=rlt.FeatureData(torch.from_numpy(np.stack(trajectory.observation)).float()),\n",
    "        action=F.one_hot(torch.from_numpy(np.stack(trajectory.action)), 2),\n",
    "        reward=torch.tensor(trajectory.reward),\n",
    "        log_prob=torch.tensor(trajectory.log_prob)\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RL Interaction Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:05.248361Z",
     "start_time": "2021-01-06T00:35:05.200070Z"
    }
   },
   "outputs": [],
   "source": [
    "from reagent.gym.runners.gymrunner import evaluate_for_n_episodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:06.268137Z",
     "start_time": "2021-01-06T00:35:05.251198Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0105 163506.153 gymrunner.py:132] For gamma=1.0, average reward is 17.11\n",
      "Rewards list: [14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23.\n",
      " 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23. 14. 23.\n",
      " 14. 23. 14. 23. 25. 13. 25. 13. 25. 13. 25. 13. 25. 13. 13. 14. 13. 14.\n",
      " 25. 13. 25. 13. 13. 14. 13. 15. 13. 14. 13. 15. 25. 13. 25. 13. 25. 13.\n",
      " 25. 13. 15. 11. 25. 13. 15. 11. 25. 13. 13. 14. 13. 15. 13. 14. 25. 13.\n",
      " 13. 15. 25. 13. 11. 10. 13. 14. 13. 14.]\n"
     ]
    }
   ],
   "source": [
    "eval_rewards = evaluate_for_n_episodes(100, env, agent, 500, num_processes=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:15.284962Z",
     "start_time": "2021-01-06T00:35:06.270524Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:08<00:00, 22.34 epoch/s, reward=197] \n"
     ]
    }
   ],
   "source": [
    "num_episodes = 200\n",
    "reward_min = 20\n",
    "max_steps = 200\n",
    "reward_decay = 0.8\n",
    "\n",
    "train_rewards = []\n",
    "running_reward = reward_min\n",
    "\n",
    "from reagent.gym.runners.gymrunner import run_episode\n",
    "\n",
    "with tqdm.trange(num_episodes, unit=\" epoch\") as t:\n",
    "    for i in t:\n",
    "        trajectory = run_episode(env, agent, max_steps=max_steps, mdp_id=i)\n",
    "        batch = to_train_batch(trajectory)\n",
    "        trainer.train(batch)\n",
    "        ep_reward = trajectory.calculate_cumulative_reward(1.0)\n",
    "        running_reward *= reward_decay\n",
    "        running_reward += (1 - reward_decay) * ep_reward\n",
    "        train_rewards.append(ep_reward)\n",
    "        t.set_postfix(reward=running_reward)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the mean reward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:17.050593Z",
     "start_time": "2021-01-06T00:35:15.286884Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0105 163516.939 gymrunner.py:132] For gamma=1.0, average reward is 200.0\n",
      "Rewards list: [200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200.]\n"
     ]
    }
   ],
   "source": [
    "eval_episodes = 200\n",
    "eval_rewards = evaluate_for_n_episodes(100, env, agent, 500, num_processes=20).T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:17.399539Z",
     "start_time": "2021-01-06T00:35:17.052835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean reward: 200.00\n"
     ]
    }
   ],
   "source": [
    "mean_reward = pd.Series(eval_rewards).mean()\n",
    "print(f'Mean reward: {mean_reward:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the rewards over training episodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:17.932189Z",
     "start_time": "2021-01-06T00:35:17.402146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140539017523344"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_rewards(rewards):\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(12, 10));\n",
    "    pd.Series(rewards).rolling(50).mean().plot(ax=ax);\n",
    "    pd.Series(rewards).plot(ax=ax,alpha=0.5,color='lightblue');\n",
    "    ax.set_xlabel('Episodes');\n",
    "    ax.set_ylabel('Reward');\n",
    "    plt.title('REINFORCE on CartPole');\n",
    "    plt.legend(['Moving Average Reward', 'Instantaneous Episode Reward'])\n",
    "    return fig, ax\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.set()\n",
    "\n",
    "\n",
    "plot_rewards(train_rewards);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-06T00:35:18.367405Z",
     "start_time": "2021-01-06T00:35:17.934338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140540647108496"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rewards(eval_rewards);\n",
    "plt.ylim([0, 210]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anp_cloned_from": {
   "revision_id": "351369499371280"
  },
  "bento_stylesheets": {
   "bento/extensions/flow/main.css": true,
   "bento/extensions/kernel_selector/main.css": true,
   "bento/extensions/kernel_ui/main.css": true,
   "bento/extensions/new_kernel/main.css": true,
   "bento/extensions/system_usage/main.css": true,
   "bento/extensions/theme/main.css": true
  },
  "kernelspec": {
   "display_name": "reagent",
   "language": "python",
   "name": "reinforcement_learning"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
